Will It Run AI

AMD

AMD Instinct MI325X 256GB

InstinctDatacenterCDNA 4OAMROCm
256GB
VRAM
6kGB/s
Bandwidth
1.3kTFLOPS
FP16 Compute
2.6kTOPS
INT8 Inference
$20,000 MSRP
VRAM256 GBBandwidth6k GB/sCompute1.3k TFInference2.6k TOPSValue6.54 TF/$k
AMD Instinct MI325X 256GBCategory AvgAMD Instinct MI350X 288GB

Operating mode

Choose the operating mode for this hardware

Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

About this GPU for AI

AMD Instinct MI325X 256GB 是 AMD 增强版基于 CDNA 4 的数据中心 GPU,提供 256 GB HBM3e 内存和 6 TB/s 带宽,在 MI300X 架构基础上增加了内存容量和带宽,面向最大规模的生产 LLM 推理工作负载。1307 TFLOPS FP16 算力配合更大内存包络,可以全精度运行最大的开源模型(405B、Llama-3-405B),无需量化。

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle — from text generation to image and video creation.

CapabilityStatusRepresentative Model
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Runs nativelyQwen 3 30B Q4
LLM Large (70B)Runs nativelyLlama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16
Image Gen (Flux)Runs nativelyFlux.1 Dev FP16
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16
Video Short (25f)Runs nativelyLTX Video 2B
Video Long (100f)Runs nativelyWan Video 14B
rocm-supporteddatacenter-gradehigh-bandwidthhigh-vramflagship

规格参数

算力
FP161307 TFLOPS
INT82614 TOPS
架构CDNA 4
显存
VRAM256 GB
带宽6000 GB/s
通用
系列Instinct
定位Datacenter
互连OAM
计算平台ROCM
MSRP$20,000

核心特性

CDNA 4 architecture (enhanced CDNA 3 platform)256 GB HBM3e across 8 stacks6 TB/s memory bandwidthImproved Matrix Core throughput with FP8/BF16/FP16AMD Infinity Fabric xGMI 3.0 multi-card interconnectFull ROCm support — production inference platform

AI 工作负载

优势
  • 256 GB HBM3e — largest single-GPU memory available for inference
  • 6 TB/s bandwidth exceeds MI300X for large-model decode throughput
  • Enables 405B models in FP16 without multi-card splitting
  • Full ROCm ecosystem support — vLLM, SGLang, PyTorch all validated
注意事项
  • Extremely expensive ($20,000+) — datacenter-only product
  • OAM form factor requires OCP/OAM server infrastructure
  • 1307 TFLOPS FP16 is similar to MI300X — gain is memory, not compute
  • NVIDIA H200 141GB offers competitive inference performance with larger CUDA ecosystem

Architecture

CDNA 4

CDNA 4 powers the next-generation Instinct MI325X and MI350X accelerators. Built on TSMC 3nm with up to 288 GB HBM3e memory and native FP4 support for maximum inference density.

AI Relevance

With up to 288 GB HBM3e and FP4 support, CDNA 4 targets the highest-density AI inference deployments. Directly competes with NVIDIA Blackwell B200 for large-scale model serving.

Process: TSMC 3nmPlatform: ROCMPrecisions: FP64, FP32, TF32, FP16, BF16, FP8, FP4, INT8

购买建议

是否应该购买 AMD Instinct MI325X 256GB 用于本地 AI?

本地 AI 的绝佳选择

能良好运行 50 个顶级模型中的 41 个 — 本地推理的全能之选。

256.0 GB

VRAM

$20,000

建议零售价

$78/GB

每 GB VRAM 成本

最适合此 GPU 的模型

What will limit you first

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best upgrade itinerary

Unlocks 1 additional models that do not fit on the current setup.

想要更多余量? AMD Instinct MI350X 288GB (288.0 GB VRAM) 是下一步升级选择。

Recommendations by Workload

Chat

S

Mistral Small 4 119B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.

Decode 190.9 tok/s · 256K ctx · llama.cppEST.
101.8 GB / 256.0 GB VRAM

Coding

S

DeepSeek V4 Flash

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface.

Decode 94.4 tok/s · 872K ctx · llama.cppEST.
185.8 GB / 256.0 GB VRAM

Agentic Coding

S

DeepSeek V4 Flash

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface.

Decode 94.4 tok/s · 872K ctx · llama.cppEST.
187.1 GB / 256.0 GB VRAM

Reasoning

S

DeepSeek V4 Flash

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface.

Decode 94.4 tok/s · 872K ctx · llama.cppEST.
185.8 GB / 256.0 GB VRAM

RAG

S

Qwen 3.5 122B A10B

This model is a direct match for rag. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.

Decode 176.1 tok/s · 131K ctx · llama.cppEST.
105.8 GB / 256.0 GB VRAM

Full Model Compatibility

DeepSeekDeepSeek V4 Flash
S99
284B185.8 GB94 tok/s872K ctx
moe
AlibabaQwen 3 235B A22B
S95
235B172.7 GB89 tok/s131K ctx
moe
AlibabaQwen 3.5 122B A10B
S93
122B103.4 GB176 tok/s131K ctx
moe
MistralDevstral 2 123B Instruct
S93
123B106.9 GB64 tok/s256K ctx
dense
MiniMax M2.7
S93
230B170.6 GB101 tok/s205K ctx
moe
MistralMistral Small 4 119B
S91
119B104.5 GB191 tok/s256K ctx
moe
OpenAIGPT-OSS 120B
S90
117B102.8 GB67 tok/s131K ctx
dense
CohereCommand A 111B
S90
111B98.1 GB71 tok/s262K ctx
dense
DeepSeekDeepSeek Coder V2 236B
S89
236B229.1 GB82 tok/s23K ctx
moe
Mistral AIPixtral Large 124B
S89
124B107.5 GB63 tok/s131K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
S89
30.5B46.6 GB662 tok/s256K ctx
moe
AlibabaQwen 2.5 VL 72B
S89
72B75.3 GB109 tok/s33K ctx
dense
AlibabaQwen 3.6 35B A3B
S88
35B52.0 GB557 tok/s262K ctx
+1moe
AlibabaQwen 3.5 27B
S88
27B46.1 GB287 tok/s131K ctx
dense
AlibabaQwen3-Coder-Next
S88
80B76.8 GB296 tok/s256K ctx
moe
AlibabaQwen 3.6 27B
S88
27B43.9 GB179 tok/s262K ctx
+1dense
AlibabaQwen3-VL 30B A3B Instruct
S88
30B46.3 GB685 tok/s256K ctx
moe
AlibabaQwen 3.5 35B A3B
S88
35B49.3 GB605 tok/s131K ctx
moe
AlibabaQwen 3 32B
S87
32B49.9 GB244 tok/s131K ctx
dense
MistralMagistral Small 2507
S87
24B43.6 GB322 tok/s131K ctx
dense
MistralDevstral Small 2 24B Instruct
S87
24B43.6 GB322 tok/s256K ctx
dense
MistralLeanstral 119B A6B
S87
119B107.9 GB176 tok/s256K ctx
moe
AlibabaQwen 3.5 9B
S86
9B34.2 GB126 tok/s131K ctx
dense
AlibabaQwen 3 30B A3B
S86
30.5B46.6 GB662 tok/s131K ctx
moe
NVIDIANemotron 3 Nano 30B
S86
30B47.2 GB257 tok/s131K ctx
dense
AlibabaQwen 3 14B
S86
14B37.5 GB196 tok/s131K ctx
dense
MistralDevstral Small 1.1
S85
24B43.6 GB322 tok/s131K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
A85
14.7B38.5 GB206 tok/s33K ctx
dense
AlibabaQwen 3 8B
A85
8B33.6 GB112 tok/s131K ctx
dense
AlibabaQwen 3.5 397B A17B
A84
397B271.5 GB39 tok/s4K ctx
moe
OpenAIGPT-OSS 20B
A84
21B41.8 GB841 tok/s128K ctx
moe
GoogleGemma 4 31B
A84
30.7B59.9 GB153 tok/s230K ctx
dense
NVIDIANemotron Cascade 2 30B A3B
A84
30B47.7 GB677 tok/s262K ctx
moe
AlibabaQwen 3.5 4B
A83
4B31.1 GB56 tok/s131K ctx
dense
LG AIEXAONE 4.0 32B
A81
32B49.9 GB242 tok/s131K ctx
dense
GoogleGemma 4 26B A4B
A81
25.2B45.5 GB711 tok/s256K ctx
moe
MistralMinistral 3 14B
A80
14B37.5 GB196 tok/s262K ctx
multimodal
MicrosoftPhi-4 Mini Reasoning 4B
A80
3.8B30.3 GB53 tok/s131K ctx
dense
NVIDIANemotron Nano 8B
A80
8B33.3 GB112 tok/s131K ctx
dense
Jina AIJina Embeddings v3
A73
0.57B29.6 GB8 tok/s8K ctx
dense
BAAIBGE M3
A73
0.57B28.8 GB8 tok/s8K ctx
dense
Moonshot AIKimi K2.5
F0
1000B643.9 GB4 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B643.9 GB4 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B890.4 GB3 tok/s4K ctx
moe
Z.aiGLM-5.1
F0
754B505.5 GB5 tok/s4K ctx
moe
Z.aiGLM-5
F0
744B499.4 GB5 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B436.3 GB8 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B322.2 GB21 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B495.4 GB6 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B495.4 GB6 tok/s4K ctx
moe

触手可及

升级后即可运行的模型

高质量模型,只需稍多一点内存

1000B100 级需要约 640.2 GB
也可运行于 4× 你的 GPU 通过 Infinity Fabric 83 tok/s
1000B100 级需要约 640.2 GB
也可运行于 4× 你的 GPU 通过 Infinity Fabric 83 tok/s
1600B100 级需要约 889.4 GB
也可运行于 4× 你的 GPU 通过 Infinity Fabric 61 tok/s
754B92 级需要约 496.0 GB
也可运行于 2× 你的 GPU 通过 Infinity Fabric 35 tok/s
744B91 级需要约 489.9 GB
也可运行于 2× 你的 GPU 通过 Infinity Fabric 37 tok/s

Image & Video Generation

Diffusion Model Compatibility

52 of 52 models can generate images or video on your AMD Instinct MI325X 256GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×5120msS
Stable Diffusion 1.5Image512×768100msS
Realistic Vision v5.1Image512×768100msS
DreamShaper 8Image512×768100msS
LCM DreamShaper v7Image512×7680msS
PixArt-SigmaImage1024×1024300msS
FramePack I2VVideo1280×720500ms/frameS
SDXL TurboImage512×5120msS
SDXL LightningImage1024×1024100msS
Stable Diffusion XL 1.0Image1024×1024300msS
Playground v2.5Image1024×1024400msS
RealVisXL v5.0Image1024×1024300msS
DreamShaper XLImage1024×1024300msS
Juggernaut XL v9Image1024×1024300msS
Animagine XL 3.1Image1024×1024300msS
Pony Diffusion V6 XLImage1024×1024300msS
Animagine XL 4.0Image1024×1024300msS
Illustrious XLImage1024×1024300msS
Wan Video 2.1 1.3BVideo480×832200ms/frameS
Stable Diffusion 3.5 MediumImage1024×1024500msS
Flux.2 Klein 4BImage1024×1024100msS
LTX Video 2BVideo1280×720200ms/frameS
KolorsImage1024×1024500msS
Stable CascadeImage1024×1024700msS
AuraFlow v0.3Image1536×1536~1.2sS
Stable Diffusion 3.5 LargeImage1024×1024~1.4sS
Stable Diffusion 3.5 Large TurboImage1024×1024300msS
CogVideoX 2BVideo720×480200ms/frameS
HunyuanVideoVideo720×1280500ms/frameS
ChromaImage1024×1024300msS
Z-Image TurboImage1536×1536300msS
Flux.1 DevImage1024×1024~1.2sS
Flux.1 SchnellImage1024×1024200msS
LTX Video 13BVideo1280×720500ms/frameS
Flux.1 Kontext DevImage1024×1024~1.3sS
AnimateDiff v1.5.3Video512×768100ms/frameS
Cosmos Diffusion 7BVideo1024×576400ms/frameS
CogVideoX 5BVideo720×480300ms/frameS
Wan2.2 TI2V 5BVideo832×480300ms/frameS
Flux.2 Klein 9BImage1024×1024100msS
Flux.1 Fill DevImage1024×1024~1.1sS
Mochi 1 PreviewVideo848×480400ms/frameS
HunyuanVideo 1.5Video720×1280400ms/frameS
Helios 14BVideo1280×720500ms/frameS
SkyReels V2 14BVideo1280×720500ms/frameS
Wan Video 2.1 14BVideo720×1280500ms/frameS
Wan Video 2.2 14BVideo720×1280500ms/frameS
Qwen ImageImage1024×1024400msS
Qwen Image EditImage1024×1024400msS
Flux.2 DevImage1024×1024~12.4sS
MAGI-1Video1280×720600ms/frameS
HunyuanImage 3.0Image1024×1024800msS

Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.

Multi-GPU scaling

AMD Instinct MI325X 256GB — Up to 8× via Infinity Fabric

Scale out with multiple GPUs for larger models. Infinity Fabric provides 896 GB/s inter-GPU bandwidth with 12% overhead.

ConfigEffective memoryModels that fitEst. bandwidth
AMD256 GB363/3746,000 GB/s
AMD512 GB371/37410,560 GB/s
AMD1024 GB374/37421,120 GB/s
AMD2048 GB374/37442,240 GB/s

Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.88× per additional GPU.

Upgrade paths

Upgrade from AMD Instinct MI325X 256GB

See what you unlock with more powerful hardware

升级选项

升级选项

Frequently Asked Questions

What AI models can I run on AMD Instinct MI325X 256GB?

AMD Instinct MI325X 256GB (256 GB VRAM) can run these top models: DeepSeek V4 Flash (score: 99/100), Qwen 3 235B A22B (score: 95/100), Qwen 3.5 122B A10B (score: 93/100). See the full compatibility list above.

How much VRAM does AMD Instinct MI325X 256GB have for AI?

AMD Instinct MI325X 256GB has 256 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is AMD Instinct MI325X 256GB good for running LLMs locally?

Yes, AMD Instinct MI325X 256GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for AMD Instinct MI325X 256GB for coding?

For coding on AMD Instinct MI325X 256GB, we recommend DeepSeek V4 Flash. It achieves 94.4 tokens per second with 872K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface.

Should I upgrade from AMD Instinct MI325X 256GB?

There are 2 upgrade path(s) from AMD Instinct MI325X 256GB: AMD Instinct MI325X 256GB, AMD Instinct MI350X 288GB. Upgrading would unlock larger models and faster inference speeds.

Can AMD Instinct MI325X 256GB run Flux for image generation?

Yes, AMD Instinct MI325X 256GB with 256 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.

What image and video AI models can I run on AMD Instinct MI325X 256GB?

AMD Instinct MI325X 256GB (256 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.

Is AMD Instinct MI325X 256GB good for AI image generation?

AMD Instinct MI325X 256GB is excellent for AI image generation. With 256 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.

Can AMD Instinct MI325X 256GB run Qwen 3.5 27B?

Yes, AMD Instinct MI325X 256GB with 256 GB of usable memory can run Qwen 3.5 27B at Q8 (near-lossless, ~28.9 GB) or even FP16 (~55.4 GB) depending on your context needs. This setup provides an excellent experience with this model. Use Ollama or vLLM for best results.

What is the best quantization for AI models on AMD Instinct MI325X 256GB?

With 256 GB VRAM on AMD Instinct MI325X 256GB, use Q8_0 for most models — it is near-lossless and you have the memory for it. For 70B+ models, Q6_K offers excellent quality. Reserve Q4_K_M for 100B+ models or when you need maximum context length.

For local LLMs on AMD Instinct MI325X 256GB, does VRAM matter more than bandwidth?

AMD Instinct MI325X 256GB already has strong memory bandwidth, so the next limit is often memory capacity and context headroom rather than raw decode speed. For local LLMs, fit first and bandwidth second is the right mental model.

How does multi-GPU scale for AI inference on AMD Instinct MI325X 256GB?

AMD Instinct MI325X 256GB supports up to 8× GPU scaling via Infinity Fabric at 896 GB/s. With 8× GPUs, you get 2048 GB effective memory with a 0.88× scaling factor per GPU. This enables running models like Kimi K2.5 and Kimi K2.6 that don't fit on a single card.

Is Infinity Fabric required for multi-GPU AMD Instinct MI325X 256GB inference?

Infinity Fabric is recommended for AMD Instinct MI325X 256GB multi-GPU inference, providing 896 GB/s interconnect bandwidth with only 12% scaling overhead. PCIe-only setups work but have higher overhead (~25%) due to limited inter-GPU bandwidth.

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